{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e4811d9d",
   "metadata": {},
   "source": [
    "# A Gentle Introduction to Forecasting in Merlion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ed1853a",
   "metadata": {},
   "source": [
    "We begin by importing Merlion's `TimeSeries` class and the data loader for the `M4` dataset. We can then divide a specific time series from this dataset into training and testing splits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "382ff20e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from merlion.utils import TimeSeries\n",
    "from ts_datasets.forecast import M4\n",
    "\n",
    "time_series, metadata = M4(subset=\"Hourly\")[0]\n",
    "train_data = TimeSeries.from_pd(time_series[metadata.trainval])\n",
    "test_data = TimeSeries.from_pd(time_series[~metadata.trainval])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d40994a",
   "metadata": {},
   "source": [
    "We can then initialize and train Merlion's `DefaultForecaster`, which is an forecasting model that balances performance with efficiency. We also obtain its predictions on the test split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "96f8384e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Inferred granularity <Hour>\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/statsmodels/base/model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/statsmodels/base/model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/statsmodels/base/model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    }
   ],
   "source": [
    "from merlion.models.defaults import DefaultForecasterConfig, DefaultForecaster\n",
    "model = DefaultForecaster(DefaultForecasterConfig())\n",
    "model.train(train_data=train_data)\n",
    "test_pred, test_err = model.forecast(time_stamps=test_data.time_stamps)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75f945ea",
   "metadata": {},
   "source": [
    "Next, we visualize the model's predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f56890b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = model.plot_forecast(time_series=test_data, plot_forecast_uncertainty=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc51ef7e",
   "metadata": {},
   "source": [
    "Finally, we quantitatively evaluate the model. sMAPE measures the error of the prediction on a scale of 0 to 100 (lower is better), while MSIS evaluates the quality of the 95% confidence band on a scale of 0 to 100 (lower is better)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "95e70579",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sMAPE: 4.1944, MSIS: 18.9331\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "from merlion.evaluate.forecast import ForecastMetric\n",
    "\n",
    "# Compute the sMAPE of the predictions (0 to 100, smaller is better)\n",
    "smape = ForecastMetric.sMAPE.value(ground_truth=test_data, predict=test_pred)\n",
    "\n",
    "# Compute the MSIS of the model's 95% confidence interval (0 to 100, smaller is better)\n",
    "lb = TimeSeries.from_pd(test_pred.to_pd() + norm.ppf(0.025) * test_err.to_pd().values)\n",
    "ub = TimeSeries.from_pd(test_pred.to_pd() + norm.ppf(0.975) * test_err.to_pd().values)\n",
    "msis = ForecastMetric.MSIS.value(ground_truth=test_data, predict=test_pred,\n",
    "                                 insample=train_data, lb=lb, ub=ub)\n",
    "print(f\"sMAPE: {smape:.4f}, MSIS: {msis:.4f}\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
